"On Corpus-Based Statistics-Oriented Approaches to Machine Translation and Two-Way Training for Knowledge Acquisition"

11/3/1997: [time not recorded]
[location not recorded]

Abstract: Knowledge acquisition and domain adaptation are the major bottlenecks in real commercialized machine translation systems; they are therefore important topics in developing an operational system. The corpus-based statistical-oriented (CBSO) approach for developing a highly parameterized MT system is thus the prospective approach to the next generation MT systems. Furthermore, traditional one-way approach (either rule-based or statistical approaches) in acquiring the translation knowledge is one major reason for producing target translations that are too literal to a native speaker. In this presentation, we therefore briefly introduce the corpus-based statistics-oriented approach to machine translation in general, and address a two-way training approach for acquiring various translation knowledge so that the translation of a source sentence falls within the grammar of the target language, and, thus, preventing the generation of literal translation.

Last updated: Mon Jun 19 17:44:06 2006